This study is guided by the intervention mapping theory, combined with Fogg's behavior model and digital health technologies, focusing on the key rehabilitation obstacles and behavioral characteristics of young and middle-aged MI patients. It constructs a "smart + collaborative" out-of-hospital rehabilitation management model. This model, through systematic needs assessment and problem logical modeling \[11\], develops individualized rehabilitation paths covering multiple dimensions of intervention such as exercise, diet, medication, and psychological regulation. It relies on digital means such as mobile terminals, online education, behavior check-ins, real-time feedback, and remote follow-ups to achieve intelligent rehabilitation management. At the same time, it emphasizes the multi-disciplinary collaboration mechanism, integrating team members from cardiology, nursing, nutrition, rehabilitation, and psychology, forming a collaborative work system with information sharing, task complementarity, and consistent goals. Together, they promote the activation of patient motivation, improvement of behavioral ability, and improvement of rehabilitation compliance. The study will also verify its feasibility and initial effects in a real clinical environment, and evaluate its practical value in improving rehabilitation behaviors, promoting functional recovery, and enhancing quality of life. This study not only innovatively integrates intervention mapping and behavioral science models into the rehabilitation management of young and middle-aged MI patients in theory, filling the gap in existing rehabilitation models for this group, but also explores the path of digital rehabilitation intervention that can be promoted and replicated in grassroots clinical practice. The research results can provide practical basis for the construction of the smart rehabilitation service system under the background of "Healthy China 2030", and have important practical significance and social value.
Patients in Ward 1 were assigned to the intervention group, and patients in Ward 2 served as the control group. The inclusion criteria were as follows: ① aged 18-60 years; ② diagnosed by cardiologists as meeting the clinical diagnostic criteria for myocardial infarction (MI) established by the Cardiology Branch of the Chinese Medical Association; ③ having basic smartphone operation skills to use the App normally; ④ providing informed consent and signing the research consent form.
① Complicated with severe hepatic or renal insufficiency, malignant tumors, cognitive impairment, or mental illnesses; ② Patients with NYHA class Ⅳ or definite exercise contraindications (e.g., those assessed as high-risk in exercise risk evaluation) who are unable to participate in exercise prescription intervention; ③ Patients who have difficulty completing follow-up or withdraw midway.